code: def specificity_score(prediction, groundtruth):
      FP, FN, TP, TN = numeric_score(prediction, groundtruth)
      if (TN + FP) <= 0.0:
          return 0.0
      TNR = np.divide(TN, TN + FP)
      return TNR * 100.0
  
file_path: medicaltorch/medicaltorch/metrics.py
imports: from collections import defaultdict
  from scipy import spatial
  import numpy
label_desc: Use image node_id 7 for calling a global function with example usage: specificity_score(prediction, groundtruth) and returns: unknown, int